Integrating time, function, phylogenetics and genome dynamics to redefine microbial diversity

Thanks to advances in DNA sequencing technology and bioinformatics, we know more about microorganisms and microbial communities than ever before. Work in the last thirty years has revealed a stunning diversity of microbes in every habitat on Earth, and researchers are now trying to connect this diversity to ecosystem function in settings as varied as wastewater reactors and the human gut. Understanding the microbes in a given habitat (the microbiome) opens up new applications including mitigation of human disease and remediation of contaminated sites.

Although this “microbial revolution” has great promise, researchers struggle to understand the microbiome in depth. The remarkable diversity of microbial species, which can exceed 1,000 in a single environmental sample, is a significant barrier to understanding the ecology of a system. Many habitats also contain millions of distinct microbial genes, a number that dwarfs the 20,000-25,000 genes in the human genome. However, the most critical limiting factor is that we don't know what patterns to look for when we examine microbial communities. Methods that presuppose natural groupings such as species inevitably miss important patterns in the data, and we need to expand our microbiomics toolbox with new representations of microbial diversity.

My research program is concerned with diversity and evolution of the microbiome. This proposal encompasses two complementary projects that will advance our ability to deconstruct and describe microbial communities. First, we will develop new ecological models that treat individual genes as species, and represent an organism as an entire ecosystem in which these genes interact. This “gene ecology” model will allow us to map traditional concepts such as diversity, dispersal, and competition into the study of microbial genomes. Second, we will develop new definitions of biodiversity units that are based on covariance through time, co-occurrence in multiple habitats, similar functions and ecological roles, as well as traditional taxonomic similarity. These new definitions will produce a prohibitively large number of candidate units, and we will develop new approaches to identify and interpret the candidates that are most relevant to a particular ecological question.

Microbial gene ecology will provide a better understanding of the complex interactions between gene products in the microbiome, and an expanded set of candidate units will help us better evaluate changes in microbial community composition. Taken together, these techniques will tell us which members of a microbial community are playing which roles, how different members of the community are interacting with one another, and how communities are likely to respond to changes in their environment. This understanding will be essential as we try to develop microbial solutions to a host of problems in human health and the environment.